This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 125
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #309508
Title: Cancer Causing Genes in Ovarian Cancer: A microarray Gene Expression of two Ovarian Cancer Cell Lines Using R 2.11.1 Bioconductor
Author(s): Jean Roayaei*+
Companies: NIH-NCI-MD
Address: 28 Miller Road, Frederick, MD, 21702,
Keywords:
Abstract:

We developed a microarray gene expression for a control patient who has low gene expression of Mic genes(2KB) vs. a case patient that has a high gene expression of Mic genes (over 1.5 gene fold induction). The control patient hasn't been given the new drug and the case patient has been administerd the new drug. We use the RMA (Robust Multi-Chip Averaging) that uses Affymetrix microarray Genechip in 3 different labs at NCI Frederick and Bethesda labs. These laboratories have access to RTPCR in their microarray laboratories. The RMA is developed by scientists at the Johns Hopkins University, School of Public Health Department of Biostatistics (i.e., Rafael Irrizary, Giovanni Parmigiani) and Robert Gentleman at the FredHutchinson Cancer Center, University of Washington. This allowed us to accurately test the hypothesis that the genes that cancer genes are up-regulated or down-regulated in ovarian cancer can be quarried in NCI large data base, NCI-60 vs. other apoptotic genes found in previous ovarian NCI studies. Our approach can accurately implicate cancer causing genes in heat map.

We used unsupervised clustering algorithm to find the optimal number of clusters by measuring the physical and genetic distance between different genes in order to put different genes in the appropriate cluster using fuzzy set algorithm (Dai-Wei Huang, Jean Roayaei, etal, Genome Biology 2007).Our method is used in DAVID bioinformatics tool developed at NCI that molecular biologists and clinicians can easily apply to their cancer research studies. We used centroid clustering method to ascertain both quantitatively and biologically the statistical significance (P-value=0.001) and biological relevance(1.5 gene fold induction) of genes implicated in ovarian cancer.

We use R 2.11.1 computing algorithms to perform microarray gene expression analysis for two ovarian cancer cell lines that are differentially expressed. These genes have been repeatable and reproducible in their different laboratories in two NIH campuses.


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